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arXiv Computer Vision

GeoGSplat: Geometry-Grounded Non-Pixel-Aligned Gaussian Splatting

GeoGSplat introduces a geometry‑grounded, feed‑forward Gaussian splatting framework that avoids the pixel‑aligned prediction bottleneck by using a fixed number of Gaussians. A dual‑branch encoder builds coarse 3D anchors and fine triplane features, while a hierarchical decoder progressively generates dense 3D points and Gaussian primitives for reconstruction and novel view synthesis. The method outperforms state‑of‑the‑art pixel‑aligned and non‑pixel‑aligned splatting techniques on DTU and NRGBD datasets, especially when rendering views far from the input cameras.

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arXiv Computer Vision
Oct 16

Anchor3DGS: Feed-Forward 3D Gaussian Splatting with Compact Anchor-Based Representation

Anchor3DGS introduces a pose‑free, feed‑forward 3D Gaussian Splatting framework that replaces pixel‑aligned Gaussian prediction with a compact anchor‑based representation. By placing 3D anchors guided by information entropy, the method concentrates primitives in visually complex regions while ensuring broad spatial coverage, and each anchor aggregates multi‑view features to generate local Gaussians. A lightweight feature enhancement module refines the coarse RGB output, resulting in state‑of‑the‑art novel view synthesis quality with an order of magnitude fewer Gaussian primitives than prior approaches.

By Sheng Ye, Jiangke Lin, Fudong Wang, Yi Yuan, Zhen-Hui Dong, Yong-Jin Liu
benchmarks
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arXiv Computer Vision
Sep 21

VoxelTTO: Voxel-Aligned Feed-Forward 3D Gaussian Splatting with Test-Time Optimization

VoxelTTO is a feed‑forward framework that reconstructs 3D Gaussian splatting scenes from multiple images by aggregating dense image features into a global voxel representation and decoding Gaussians from voxel features, thereby eliminating the pixel‑to‑Gaussian correspondence. It incorporates test‑time optimization with lightweight LoRA modules to adapt to known camera parameters while keeping the pretrained visual foundation model frozen. The method replaces standard rasterization with stochastic solid volume rendering, improving geometric fidelity, and demonstrates superior RGB‑D novel‑view synthesis and camera‑pose estimation on Replica, Tanks and Temples, and DTU datasets.

By Yibin Zhao, Yihan Pan, Yangwen Li, Jun Nan, Jianjun Yi
computer-visionfine-tuning
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arXiv Computer Vision
Sep 3

PointGauss: Point Cloud-Guided Multi-Object Segmentation for Gaussian Splatting

PointGauss is a 3D-native framework that performs semantic parsing and instance segmentation on 3D Gaussian splatting representations by treating Gaussian primitives as unstructured point sets and extracting scale‑invariant geometric features with Point Transformer V3. It introduces an adaptive region‑of‑interest cropping strategy and an instance‑aware distance‑constrained rasterization pipeline to enable scalable, view‑consistent pixel‑level projections. The authors also release SplatSeg‑360, a cross‑scale benchmark with 32 complex scenes and over 6,300 aligned 2D‑3D masks, and show that PointGauss achieves real‑time performance with state‑of‑the‑art 3D‑mIoU (~90%) and 2D‑mIoU (~80%) scores.

By Wentao Sun, Yiping Chen, John S. Zelek, Jonathan Li
llmscomputer-visionefficiencybenchmarks
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Hugging Face Trending Papers
Aug 3

D^2-4DGS: Dual-Depth Guided Sparse-Camera 4D Gaussian Splatting

Dynamic 4D Gaussian Splatting has emerged as an efficient representation for dynamic novel view synthesis through explicit scene modeling and real-time rendering. However, existing methods typically require dense multi-view videos for sufficient geometric constraints, making capture expensive and limiting sparse-camera deployment.

efficiency
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Hugging Face Trending Papers
Aug 19

CoMVS-GS: Collaborative Multi-View Stereo and 3D Gaussian Splatting for Surface Reconstruction

CoMVS‑GS is a surface‑reconstruction framework that fuses Multi‑View Stereo (MVS) with 3D Gaussian splatting. It initializes Gaussian primitives from dense MVS points, uses PatchMatch‑3DGS mutual supervision to refine depths and normals, and replaces voxel‑based meshing with a Delaunay graph‑cut pipeline. Experiments on DTU, GauU‑Scene V2, and MatrixCity demonstrate competitive object‑level results and improved geometric accuracy and mesh compactness in outdoor scenes while preserving high rendering quality.

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